Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

Background and purpose: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. / Methods: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. / Results: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. / Conclusion: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features

Sex differences in cardiovascular complications and mortality in hospital patients with covid-19: registry based observational study

Objective To assess whether the risk of cardiovascular complications of covid-19 differ between the sexes and to determine whether any sex differences in risk are reduced in individuals with pre-existing cardiovascular disease. Design Registry based observational study. Setting 74 hospitals across 13 countries (eight European) participating in CAPACITY-COVID (Cardiac complicAtions in Patients With SARS Corona vIrus 2 regisTrY), from March 2020 to May 2021 Participants All adults (aged ≥18 years), predominantly European, admitted to hospital with highly suspected covid-19 disease or covid-19 disease confirmed by positive laboratory test results (n=11 167 patients). Main outcome measures Any cardiovascular complication during admission to hospital. Secondary outcomes were in-hospital mortality and individual cardiovascular complications with ≥20 events for each sex. Logistic regression was used to examine sex differences in the risk of cardiovascular outcomes, overall and grouped by pre-existing cardiovascular disease. Results Of 11 167 adults (median age 68 years, 40% female participants) included, 3423 (36% of whom were female participants) had pre-existing cardiovascular disease. In both sexes, the most common cardiovascular complications were supraventricular tachycardias (4% of female participants, 6% of male participants), pulmonary embolism (3% and 5%), and heart failure (decompensated or de novo) (2% in both sexes). After adjusting for age, ethnic group, pre-existing cardiovascular disease, and risk factors for cardiovascular disease, female individuals were less likely than male individuals to have a cardiovascular complication (odds ratio 0.72, 95% confidence interval 0.64 to 0.80) or die (0.65, 0.59 to 0.72). Differences between the sexes were not modified by pre-existing cardiovascular disease; for the primary outcome, the female-to-male ratio of the odds ratio in those without, compared with those with, pre-existing cardiovascular disease was 0.84 (0.67 to 1.07). Conclusions In patients admitted to hospital for covid-19, female participants were less likely than male participants to have a cardiovascular complication. The differences between the sexes could not be attributed to the lower prevalence of pre-existing cardiovascular disease in female individuals. The reasons for this advantage in female individuals requires further research

Common genetic variants contribute to risk of transposition of the great arteries

Rationale: Dextro-transposition of the great arteries (D-TGA) is a severe congenital heart defect which affects approximately 1 in 4,000 live births. While there are several reports of D-TGA patients with rare variants in individual genes, the majority of D-TGA cases remain genetically elusive. Familial recurrence patterns and the observation that most cases with D-TGA are sporadic suggest a polygenic inheritance for the disorder, yet this remains unexplored. Objective: We sought to study the role of common single nucleotide polymorphisms (SNPs) in risk for D-TGA. Methods and Results: We conducted a genome-wide association study in an international set of 1,237 patients with D-TGA and identified a genome-wide significant susceptibility locus on chromosome 3p14.3, which was subsequently replicated in an independent case-control set (rs56219800, meta-analysis P=8.6x10-10, OR=0.69 per C allele). SNP-based heritability analysis showed that 25% of variance in susceptibility to D-TGA may be explained by common variants. A genome-wide polygenic risk score derived from the discovery set was significantly associated to D-TGA in the replication set (P=4x10-5). The genome-wide significant locus (3p14.3) co-localizes with a putative regulatory element that interacts with the promoter of WNT5A, which encodes the Wnt Family Member 5A protein known for its role in cardiac development in mice. We show that this element drives reporter gene activity in the developing heart of mice and zebrafish and is bound by the developmental transcription factor TBX20. We further demonstrate that TBX20 attenuates Wnt5a expression levels in the developing mouse heart. Conclusions: This work provides support for a polygenic architecture in D-TGA and identifies a susceptibility locus on chromosome 3p14.3 near WNT5A. Genomic and functional data support a causal role of WNT5A at the locus

Electrocardiogram-based mortality prediction in patients with COVID-19 using machine learning

BACKGROUND AND PURPOSE: The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. METHODS: Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals (n = 634), three models were developed: (a) a logistic regression baseline model using age and sex, (b) a least absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c) a pre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals (n = 248) was used for external validation. RESULTS: Performances for models a, b and c were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. CONCLUSION: This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features. SUPPLEMENTARY INFORMATION: The online version of this article (10.1007/s12471-022-01670-2) contains supplementary material, which is available to authorized users